Causal Discovery from a Mixture of Experimental and Observational Data

By Gregory F. Cooper, Changwon Yoo. 1999

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Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Furthermore, we show that DAG-GFlowNet can be applied on both observational and interventional data, by modifying standard Bayesian scores (Cooper and Yoo, 1999).”
Learning Neural Causal Models from Unknown Interventions (2019, methodology): “…possible directed acyclic graphs (DAGs) representing the causal structure based on some form of scoring function for network structures (Chickering, 2002; Tsamardinos et al., 2006; Goudet et al., 2017; Hauser & Bühlmann, 2012; Heckerman et al., 1995; Cooper & Yoo, 1999; Hauser & Bühlmann, 2012).”
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs (2011, methodology): “The Bayesian procedures of Cooper and Yoo (1999) or Eaton and Murphy (2007) address the problem of calculating a posterior (and also a likelihood) of an ensemble of observational and interventional data but do not address questions of identiﬁability or Markov equivalence: allowing diﬀerent…”
Causal Structure Learning (2017, methodology): “Other excluded methods that make use of interventional data include Cooper and Yoo (1999); Tian and Pearl (2001) and Eaton and Murphy (2007), where the latter does not require knowledge of the precise location of interventions in a similar spirit to Rothenh¨ausler et al. (2015).”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “ity, they do sounderspecificationofacausallysufficientgraph. Futureresearch in this direction may explore less strict settings, perhaps accounting for hidden confounders, or partially observed graphs [9, 44, 168], further adding to the uncertainy of recourse recommendations. Alternatively, sources o”
Experiment selection for causal discovery (2013, methodology): “…recent literature on causal discovery, randomized experiments, in combination with novel inference principles, play an increasingly prominent role (Cooper and Yoo, 1999; Tong and Koller, 2001; Murphy, 2001; Eaton and Murphy, 2007; Meganck et al., 2005; Eberhardt et al., 2005; He and Geng, 2008;…”
Learning Bayesian networks: approaches and issues (2011, methodology): “Other implementations of classification using Bayesian networks include those by Correa et al. (2007), who use them in the classification stage of an algorithm that also features attribute selection using a discrete particle-swarm optimization algorithm and Cheng and Greiner (1999) who compare class”
Probabilistic Soft Interventions in Conditional Gaussian Networks (2005, methodology): “This special case was already shown in (Cooper and Yoo, 1999; Tian and Pearl, 2001).”
Causal Discovery from Changes: a Bayesian Approach (2001, methodology): “These discovery methods assume static environment,that is, a time-invariant distribution and a time-invariant data-generating model, while attempting toinfer structures that encode dynamic aspects of theenvironment, for example, how probabilities wouldchange as a result of interventions.”
Constraint-based causal discovery from multiple interventions over overlapping variable sets (2014, methodology): “MAGs and SMCMs are graphical models that represent both causal relations and conditional independencies among a set of measured (observed) variables O, and can be viewed as generalizations of causal Bayesian networks that can account for latent confounders.”
Almost Optimal Intervention Sets for Causal Discovery (2008, background): “Tong & Koller (2001), Murphy (2001) and Cooper and Yoo (1999) have presented Bayesian approaches on how to select the next experiment given an OME or MME.”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “Cooper and Yoo (1999) proposed a Bayesian scoring metric that can incorporate both observational and experimental data.”
Bayesian structure learning using dynamic programming and MCMC (2007, background): “This can be modified to handle interventional (experimental) data using the simple trick in (3).”
Learning linear cyclic causal models with latent variables (2012, methodology): “…the learning procedure by Geiger and Heckerman (1994) for acyclic Bayesian networks with linear Gaussian conditional probability distributions, referred to as GH. Experimental data is incorporated into the calculation of the local scores in GH using the technique described by Cooper and Yoo (1999).”
Statistics and causal inference: A review (2003, background): “Can this be maintained if the errors associated to X and to Y given X are not independent? This causal interpretation is more evident in procedures using observational and experimental data at the same time (Cooper and Yoo, 1999), in which the truncated factorization is used in the score derivation.”